Nothing
inst/doc/Vignettes.R
In influential: Identification and Classification of the Most Influential Nodes
## ----include = FALSE--------------------------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
options(tibble.print_min = 4L, tibble.print_max = 4L)
options(width=90)
options(rmarkdown.html_vignette.check_title = FALSE)
set.seed(60)
## ----echo=FALSE-------------------------------------------------------------------------
spaces <- function (n) {
paste(rep(" ", n), collapse = "")
}
## ----fig.align="right", echo=FALSE, out.width="25%", out.extra='style="float:right; padding:10px"'----
knitr::include_graphics(path = "../man/figures/Symbol.png", error = FALSE)
## ----setup------------------------------------------------------------------------------
library(influential)
## ----exptl_data_fcor, eval=FALSE--------------------------------------------------------
#
# # Prepare a sample dataset
# set.seed(60)
# my_data <- matrix(data = runif(n = 10000, min = 2, max = 300),
# nrow = 50, ncol = 200,
# dimnames = list(c(paste("sample", c(1:50), sep = "_")),
# c(paste("gene", c(1:200), sep = "_")))
# )
## ----fcor_calc, eval=FALSE--------------------------------------------------------------
#
# # Calculate correlations between all pairs of genes
#
# correlation_tbl <- fcor(data = my_data,
# method = "spearman",
# mutualRank = TRUE,
# pvalue = "TRUE", adjust = "BH",
# flat = TRUE)
## ----echo=FALSE-------------------------------------------------------------------------
knitr::kable(head(coexpression.data))
## ----g_dataframe------------------------------------------------------------------------
# Preparing the data
MyData <- coexpression.data
# Reconstructing the graph
My_graph <- graph_from_data_frame(d=MyData)
## ---------------------------------------------------------------------------------------
class(My_graph)
## ----echo=FALSE-------------------------------------------------------------------------
knitr::kable(head(coexpression.adjacency, n=15)[10:15,10:15])
## ----g_adj, eval=FALSE------------------------------------------------------------------
# # Preparing the data
# MyData <- coexpression.adjacency
#
# # Reconstructing the graph
# My_graph <- graph_from_adjacency_matrix(MyData)
## ----echo=FALSE-------------------------------------------------------------------------
set.seed(60)
My_Data <- matrix(data = sample(c(0,1), replace = TRUE, size = 20),
nrow = 4, ncol = 5,
dimnames = list(c(paste("cell", c(1:4), sep = "_")),
c(paste("Gene", c(1:5), sep = "_"))))
knitr::kable(My_Data)
## ----g_inc, eval=FALSE------------------------------------------------------------------
# # Reconstructing the graph
# My_graph <- graph_from_adjacency_matrix(MyData)
## ----g_sif, eval=FALSE------------------------------------------------------------------
# # Reconstructing the graph
# My_graph <- sif2igraph(Path = "Sample_SIF.sif")
#
# class(My_graph)
# #> [1] "igraph"
## ----Vertices, eval=FALSE---------------------------------------------------------------
# # Preparing the data
# MyData <- coexpression.data
#
# # Reconstructing the graph
# My_graph <- graph_from_data_frame(MyData)
#
# # Extracting the vertices
# My_graph_vertices <- V(My_graph)
#
# head(My_graph_vertices)
# #> + 6/794 vertices, named, from 775cff6:
# #> [1] ADAMTS9-AS2 C8orf34-AS1 CADM3-AS1 FAM83A-AS1 FENDRR LANCL1-AS1
## ----DC, eval=FALSE---------------------------------------------------------------------
# # Preparing the data
# MyData <- coexpression.data
#
# # Reconstructing the graph
# My_graph <- graph_from_data_frame(MyData)
#
# # Extracting the vertices
# GraphVertices <- V(My_graph)
#
# # Calculating degree centrality
# My_graph_degree <- degree(My_graph, v = GraphVertices, normalized = FALSE)
#
# head(My_graph_degree)
# #> ADAMTS9-AS2 C8orf34-AS1 CADM3-AS1 FAM83A-AS1 FENDRR LANCL1-AS1
# #> 172 121 168 26 189 176
## ----BC, eval=FALSE---------------------------------------------------------------------
# # Preparing the data
# MyData <- coexpression.data
#
# # Reconstructing the graph
# My_graph <- graph_from_data_frame(MyData)
#
# # Extracting the vertices
# GraphVertices <- V(My_graph)
#
# # Calculating betweenness centrality
# My_graph_betweenness <- betweenness(My_graph, v = GraphVertices,
# directed = FALSE, normalized = FALSE)
#
# head(My_graph_betweenness)
# #> ADAMTS9-AS2 C8orf34-AS1 CADM3-AS1 FAM83A-AS1 FENDRR LANCL1-AS1
# #> 21719.857 28185.199 26946.625 2940.467 33333.369 21830.511
## ----NC, eval=FALSE---------------------------------------------------------------------
# # Preparing the data
# MyData <- coexpression.data
#
# # Reconstructing the graph
# My_graph <- graph_from_data_frame(MyData)
#
# # Extracting the vertices
# GraphVertices <- V(My_graph)
#
# # Calculating neighborhood connectivity
# neighrhood.co <- neighborhood.connectivity(graph = My_graph,
# vertices = GraphVertices,
# mode = "all")
#
# head(neighrhood.co)
# #> ADAMTS9-AS2 C8orf34-AS1 CADM3-AS1 FAM83A-AS1 FENDRR LANCL1-AS1
# #> 11.290698 4.983471 7.970238 3.000000 15.153439 13.465909
## ----H_index, eval=FALSE----------------------------------------------------------------
# # Preparing the data
# MyData <- coexpression.data
#
# # Reconstructing the graph
# My_graph <- graph_from_data_frame(MyData)
#
# # Extracting the vertices
# GraphVertices <- V(My_graph)
#
# # Calculating H-index
# h.index <- h_index(graph = My_graph,
# vertices = GraphVertices,
# mode = "all")
#
# head(h.index)
# #> ADAMTS9-AS2 C8orf34-AS1 CADM3-AS1 FAM83A-AS1 FENDRR LANCL1-AS1
# #> 11 9 11 2 12 12
## ----LH_index, eval=FALSE---------------------------------------------------------------
# # Preparing the data
# MyData <- coexpression.data
#
# # Reconstructing the graph
# My_graph <- graph_from_data_frame(MyData)
#
# # Extracting the vertices
# GraphVertices <- V(My_graph)
#
# # Calculating Local H-index
# lh.index <- lh_index(graph = My_graph,
# vertices = GraphVertices,
# mode = "all")
#
# head(lh.index)
# #> ADAMTS9-AS2 C8orf34-AS1 CADM3-AS1 FAM83A-AS1 FENDRR LANCL1-AS1
# #> 1165 446 994 34 1289 1265
## ----CI, eval=FALSE---------------------------------------------------------------------
# # Preparing the data
# MyData <- coexpression.data
#
# # Reconstructing the graph
# My_graph <- graph_from_data_frame(MyData)
#
# # Extracting the vertices
# GraphVertices <- V(My_graph)
#
# # Calculating Collective Influence
# ci <- collective.influence(graph = My_graph,
# vertices = GraphVertices,
# mode = "all", d=3)
#
# head(ci)
# #> ADAMTS9-AS2 C8orf34-AS1 CADM3-AS1 FAM83A-AS1 FENDRR LANCL1-AS1
# #> 9918 70560 39078 675 10716 7350
## ----CR, eval=FALSE---------------------------------------------------------------------
# # Preparing the data
# MyData <- coexpression.data
#
# # Reconstructing the graph
# My_graph <- graph_from_data_frame(MyData)
#
# # Extracting the vertices
# GraphVertices <- V(My_graph)
#
# # Calculating ClusterRank
# cr <- clusterRank(graph = My_graph,
# vids = GraphVertices,
# directed = FALSE, loops = TRUE)
#
# head(cr)
# #> ADAMTS9-AS2 C8orf34-AS1 CADM3-AS1 FAM83A-AS1 FENDRR LANCL1-AS1
# #> 63.459812 5.185675 21.111776 1.280000 135.098278 81.255195
## ----cond.prob--------------------------------------------------------------------------
# Preparing the data
MyData <- centrality.measures
# Assessing the conditional probability
My.conditional.prob <- cond.prob.analysis(data = MyData,
nodes.colname = rownames(MyData),
Desired.colname = "BC",
Condition.colname = "NC")
print(My.conditional.prob)
## ----double.cent.assess, eval=FALSE-----------------------------------------------------
# # Preparing the data
# MyData <- centrality.measures
#
# # Association assessment
# My.metrics.assessment <- double.cent.assess(data = MyData,
# nodes.colname = rownames(MyData),
# dependent.colname = "BC",
# independent.colname = "NC")
#
# print(My.metrics.assessment)
# #> $Summary_statistics
# #> BC NC
# #> Min. 0.000000000 1.2000
# #> 1st Qu. 0.000000000 66.0000
# #> Median 0.000000000 156.0000
# #> Mean 0.005813357 132.3443
# #> 3rd Qu. 0.000340000 179.3214
# #> Max. 0.529464720 192.0000
# #>
# #> $Normality_results
# #> p.value
# #> BC 1.415450e-50
# #> NC 9.411737e-30
# #>
# #> $Dependent_Normality
# #> [1] "Non-normally distributed"
# #>
# #> $Independent_Normality
# #> [1] "Non-normally distributed"
# #>
# #> $GAM_nonlinear.nonmonotonic.results
# #> edf p-value
# #> 8.992406 0.000000
# #>
# #> $Association_type
# #> [1] "nonlinear-nonmonotonic"
# #>
# #> $HoeffdingD_Statistic
# #> D_statistic P_value
# #> Results 0.01770279 1e-08
# #>
# #> $Dependence_Significance
# #> Hoeffding
# #> Results Significantly dependent
# #>
# #> $NNS_dep_results
# #> Correlation Dependence
# #> Results -0.7948106 0.8647164
# #>
# #> $ConditionalProbability
# #> [1] 55.35386
# #>
# #> $ConditionalProbability_split.half.sample
# #> [1] 55.90331
## ----double.cent.assess.noRegr., eval=FALSE---------------------------------------------
# # Preparing the data
# MyData <- centrality.measures
#
# # Association assessment
# My.metrics.assessment <- double.cent.assess.noRegression(data = MyData,
# nodes.colname = rownames(MyData),
# centrality1.colname = "BC",
# centrality2.colname = "NC")
#
# print(My.metrics.assessment)
# #> $Summary_statistics
# #> BC NC
# #> Min. 0.000000000 1.2000
# #> 1st Qu. 0.000000000 66.0000
# #> Median 0.000000000 156.0000
# #> Mean 0.005813357 132.3443
# #> 3rd Qu. 0.000340000 179.3214
# #> Max. 0.529464720 192.0000
# #>
# #> $Normality_results
# #> p.value
# #> BC 1.415450e-50
# #> NC 9.411737e-30
# #>
# #> $Centrality1_Normality
# #> [1] "Non-normally distributed"
# #>
# #> $Centrality2_Normality
# #> [1] "Non-normally distributed"
# #>
# #> $HoeffdingD_Statistic
# #> D_statistic P_value
# #> Results 0.01770279 1e-08
# #>
# #> $Dependence_Significance
# #> Hoeffding
# #> Results Significantly dependent
# #>
# #> $NNS_dep_results
# #> Correlation Dependence
# #> Results -0.7948106 0.8647164
# #>
# #> $ConditionalProbability
# #> [1] 55.35386
# #>
# #> $ConditionalProbability_split.half.sample
# #> [1] 55.68163
## ----IVI.from.indices, eval=FALSE-------------------------------------------------------
# # Preparing the data
# MyData <- centrality.measures
#
# # Calculation of IVI
# My.vertices.IVI <- ivi.from.indices(DC = MyData$DC,
# CR = MyData$CR,
# NC = MyData$NC,
# LH_index = MyData$LH_index,
# BC = MyData$BC,
# CI = MyData$CI)
#
# head(My.vertices.IVI)
# #> [1] 24.670056 8.344337 18.621049 1.017768 29.437028 33.512598
## ----IVI, eval=FALSE--------------------------------------------------------------------
# # Preparing the data
# MyData <- coexpression.data
#
# # Reconstructing the graph
# My_graph <- graph_from_data_frame(MyData)
#
# # Extracting the vertices
# GraphVertices <- V(My_graph)
#
# # Calculation of IVI
# My.vertices.IVI <- ivi(graph = My_graph, vertices = GraphVertices,
# weights = NULL, directed = FALSE, mode = "all",
# loops = TRUE, d = 3, scale = "range")
#
# head(My.vertices.IVI)
# #> ADAMTS9-AS2 C8orf34-AS1 CADM3-AS1 FAM83A-AS1 FENDRR LANCL1-AS1
# #> 39.53878 19.94999 38.20524 1.12371 100.00000 47.49356
## ----net.for.vis, eval=FALSE------------------------------------------------------------
# # Reconstructing the graph
# set.seed(70)
# My_graph <- igraph::sample_gnm(n = 50, m = 120, directed = TRUE)
#
# # Calculating the IVI values
# My_graph_IVI <- ivi(My_graph, directed = TRUE)
#
# # Visualizing the graph based on IVI values
# My_graph_IVI_Vis <- cent_network.vis(graph = My_graph,
# cent.metric = My_graph_IVI,
# directed = TRUE,
# plot.title = "IVI-based Network",
# legend.title = "IVI value")
#
# My_graph_IVI_Vis
## ----Spreading.score, eval=FALSE--------------------------------------------------------
# # Preparing the data
# MyData <- coexpression.data
#
# # Reconstructing the graph
# My_graph <- graph_from_data_frame(MyData)
#
# # Extracting the vertices
# GraphVertices <- V(My_graph)
#
# # Calculation of Spreading score
# Spreading.score <- spreading.score(graph = My_graph,
# vertices = GraphVertices,
# weights = NULL, directed = FALSE, mode = "all",
# loops = TRUE, d = 3, scale = "range")
#
# head(Spreading.score)
# #> ADAMTS9-AS2 C8orf34-AS1 CADM3-AS1 FAM83A-AS1 FENDRR LANCL1-AS1
# #> 42.932497 38.094111 45.114648 1.587262 100.000000 49.193292
## ----Hubness.score, eval=FALSE----------------------------------------------------------
# # Preparing the data
# MyData <- coexpression.data
#
# # Reconstructing the graph
# My_graph <- graph_from_data_frame(MyData)
#
# # Extracting the vertices
# GraphVertices <- V(My_graph)
#
# # Calculation of Hubness score
# Hubness.score <- hubness.score(graph = My_graph,
# vertices = GraphVertices,
# directed = FALSE, mode = "all",
# loops = TRUE, scale = "range")
#
# head(Hubness.score)
# #> ADAMTS9-AS2 C8orf34-AS1 CADM3-AS1 FAM83A-AS1 FENDRR LANCL1-AS1
# #> 84.299719 46.741660 77.441514 8.437142 92.870451 88.734131
## ----SIRIR, eval=FALSE------------------------------------------------------------------
# # Reconstructing the graph
# My_graph <- sif2igraph(Path = "Sample_SIF.sif")
#
# # Extracting the vertices
# GraphVertices <- V(My_graph)
#
# # Calculation of influence rank
# Influence.Ranks <- sirir(graph = My_graph,
# vertices = GraphVertices,
# beta = 0.5, gamma = 1, no.sim = 10, seed = 1234)
#
## ----exir.data, eval=FALSE--------------------------------------------------------------
# # Prepare sample data
# gene.names <- paste("gene", c(1:2000), sep = "_")
#
# set.seed(60)
# tp2.vs.tp1.DEGs <- data.frame(logFC = rnorm(n = 700, mean = 2, sd = 4),
# FDR = runif(n = 700, min = 0.0001, max = 0.049))
#
# set.seed(60)
# rownames(tp2.vs.tp1.DEGs) <- sample(gene.names, size = 700)
#
# set.seed(70)
# tp3.vs.tp2.DEGs <- data.frame(logFC = rnorm(n = 1300, mean = -1, sd = 5),
# FDR = runif(n = 1300, min = 0.0011, max = 0.039))
#
# set.seed(70)
# rownames(tp3.vs.tp2.DEGs) <- sample(gene.names, size = 1300)
#
# set.seed(80)
# regression.data <- data.frame(R_squared = runif(n = 800, min = 0.1, max = 0.85))
#
# set.seed(80)
# rownames(regression.data) <- sample(gene.names, size = 800)
## ----diff_data_assembl, eval=FALSE------------------------------------------------------
# my_Diff_data <- diff_data.assembly(tp2.vs.tp1.DEGs,
# tp3.vs.tp2.DEGs,
# regression.data)
#
# my_Diff_data[c(1:10),]
## ----exptl_data, eval=FALSE-------------------------------------------------------------
# set.seed(60)
# MyExptl_data <- matrix(data = runif(n = 100000, min = 2, max = 300),
# nrow = 50, ncol = 2000,
# dimnames = list(c(paste("cancer_sample", c(1:25), sep = "_"),
# paste("normal_sample", c(1:25), sep = "_")),
# gene.names))
#
# # Log transform the data to bring them closer to normal distribution
# MyExptl_data <- log2(MyExptl_data)
#
# MyExptl_data[c(1:5, 45:50),c(1:5)]
## ----condition.col, eval=FALSE----------------------------------------------------------
# MyExptl_data <- as.data.frame(MyExptl_data)
# MyExptl_data$condition <- c(rep("C", 25), rep("N", 25))
## ----ExIR, eval=FALSE-------------------------------------------------------------------
#
# #The table of differential/regression previously prepared
# my_Diff_data
#
# #The column indices of differential values in the Diff_data table
# Diff_value <- c(1,3)
#
# #The column indices of regression values in the Diff_data table
# Regr_value <- 5
#
# #The column indices of significance (P-value/FDR) values in
# # the Diff_data table
# Sig_value <- c(2,4)
#
# #The matrix/data frame of normalized experimental
# # data previously prepared
# MyExptl_data
#
# #The name of the column delineating the conditions of
# # samples in the Exptl_data matrix
# Condition_colname <- "condition"
#
# #The desired list of features
# set.seed(60)
# MyDesired_list <- sample(gene.names, size = 500) #Optional
#
# #Running the ExIR model
# My.exir <- exir(Desired_list = MyDesired_list,
# cor_thresh_method = "mr", mr = 100,
# Diff_data = my_Diff_data, Diff_value = Diff_value,
# Regr_value = Regr_value, Sig_value = Sig_value,
# Exptl_data = MyExptl_data, Condition_colname = Condition_colname,
# seed = 60, verbose = FALSE)
#
# names(My.exir)
# #> [1] "Driver table" "DE-mediator table" "Biomarker table" "Graph"
#
# class(My.exir)
# #> [1] "ExIR_Result"
## ----exir.vis, eval=FALSE---------------------------------------------------------------
# My.exir.Vis <- exir.vis(exir.results = My.exir,
# n = 5,
# y.axis.title = "Gene")
#
# My.exir.Vis
## ----comp_manipulate, eval=FALSE--------------------------------------------------------
# # Select which genes to knockout
# set.seed(60)
# ko_vertices <- sample(igraph::as_ids(V(My.exir$Graph)), size = 5)
#
# # Select which genes to up-regulate
# set.seed(1234)
# upregulate_vertices <- sample(igraph::as_ids(V(My.exir$Graph)), size = 5)
#
# Computational_manipulation <- comp_manipulate(exir_output = My.exir,
# ko_vertices = ko_vertices,
# upregulate_vertices = upregulate_vertices,
# beta = 0.5, gamma = 1, no.sim = 100, seed = 1234)
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## ----include = FALSE--------------------------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
options(tibble.print_min = 4L, tibble.print_max = 4L)
options(width=90)
options(rmarkdown.html_vignette.check_title = FALSE)
set.seed(60)
## ----echo=FALSE-------------------------------------------------------------------------
spaces <- function (n) {
paste(rep(" ", n), collapse = "")
}
## ----fig.align="right", echo=FALSE, out.width="25%", out.extra='style="float:right; padding:10px"'----
knitr::include_graphics(path = "../man/figures/Symbol.png", error = FALSE)
## ----setup------------------------------------------------------------------------------
library(influential)
## ----exptl_data_fcor, eval=FALSE--------------------------------------------------------
#
# # Prepare a sample dataset
# set.seed(60)
# my_data <- matrix(data = runif(n = 10000, min = 2, max = 300),
# nrow = 50, ncol = 200,
# dimnames = list(c(paste("sample", c(1:50), sep = "_")),
# c(paste("gene", c(1:200), sep = "_")))
# )
## ----fcor_calc, eval=FALSE--------------------------------------------------------------
#
# # Calculate correlations between all pairs of genes
#
# correlation_tbl <- fcor(data = my_data,
# method = "spearman",
# mutualRank = TRUE,
# pvalue = "TRUE", adjust = "BH",
# flat = TRUE)
## ----echo=FALSE-------------------------------------------------------------------------
knitr::kable(head(coexpression.data))
## ----g_dataframe------------------------------------------------------------------------
# Preparing the data
MyData <- coexpression.data
# Reconstructing the graph
My_graph <- graph_from_data_frame(d=MyData)
## ---------------------------------------------------------------------------------------
class(My_graph)
## ----echo=FALSE-------------------------------------------------------------------------
knitr::kable(head(coexpression.adjacency, n=15)[10:15,10:15])
## ----g_adj, eval=FALSE------------------------------------------------------------------
# # Preparing the data
# MyData <- coexpression.adjacency
#
# # Reconstructing the graph
# My_graph <- graph_from_adjacency_matrix(MyData)
## ----echo=FALSE-------------------------------------------------------------------------
set.seed(60)
My_Data <- matrix(data = sample(c(0,1), replace = TRUE, size = 20),
nrow = 4, ncol = 5,
dimnames = list(c(paste("cell", c(1:4), sep = "_")),
c(paste("Gene", c(1:5), sep = "_"))))
knitr::kable(My_Data)
## ----g_inc, eval=FALSE------------------------------------------------------------------
# # Reconstructing the graph
# My_graph <- graph_from_adjacency_matrix(MyData)
## ----g_sif, eval=FALSE------------------------------------------------------------------
# # Reconstructing the graph
# My_graph <- sif2igraph(Path = "Sample_SIF.sif")
#
# class(My_graph)
# #> [1] "igraph"
## ----Vertices, eval=FALSE---------------------------------------------------------------
# # Preparing the data
# MyData <- coexpression.data
#
# # Reconstructing the graph
# My_graph <- graph_from_data_frame(MyData)
#
# # Extracting the vertices
# My_graph_vertices <- V(My_graph)
#
# head(My_graph_vertices)
# #> + 6/794 vertices, named, from 775cff6:
# #> [1] ADAMTS9-AS2 C8orf34-AS1 CADM3-AS1 FAM83A-AS1 FENDRR LANCL1-AS1
## ----DC, eval=FALSE---------------------------------------------------------------------
# # Preparing the data
# MyData <- coexpression.data
#
# # Reconstructing the graph
# My_graph <- graph_from_data_frame(MyData)
#
# # Extracting the vertices
# GraphVertices <- V(My_graph)
#
# # Calculating degree centrality
# My_graph_degree <- degree(My_graph, v = GraphVertices, normalized = FALSE)
#
# head(My_graph_degree)
# #> ADAMTS9-AS2 C8orf34-AS1 CADM3-AS1 FAM83A-AS1 FENDRR LANCL1-AS1
# #> 172 121 168 26 189 176
## ----BC, eval=FALSE---------------------------------------------------------------------
# # Preparing the data
# MyData <- coexpression.data
#
# # Reconstructing the graph
# My_graph <- graph_from_data_frame(MyData)
#
# # Extracting the vertices
# GraphVertices <- V(My_graph)
#
# # Calculating betweenness centrality
# My_graph_betweenness <- betweenness(My_graph, v = GraphVertices,
# directed = FALSE, normalized = FALSE)
#
# head(My_graph_betweenness)
# #> ADAMTS9-AS2 C8orf34-AS1 CADM3-AS1 FAM83A-AS1 FENDRR LANCL1-AS1
# #> 21719.857 28185.199 26946.625 2940.467 33333.369 21830.511
## ----NC, eval=FALSE---------------------------------------------------------------------
# # Preparing the data
# MyData <- coexpression.data
#
# # Reconstructing the graph
# My_graph <- graph_from_data_frame(MyData)
#
# # Extracting the vertices
# GraphVertices <- V(My_graph)
#
# # Calculating neighborhood connectivity
# neighrhood.co <- neighborhood.connectivity(graph = My_graph,
# vertices = GraphVertices,
# mode = "all")
#
# head(neighrhood.co)
# #> ADAMTS9-AS2 C8orf34-AS1 CADM3-AS1 FAM83A-AS1 FENDRR LANCL1-AS1
# #> 11.290698 4.983471 7.970238 3.000000 15.153439 13.465909
## ----H_index, eval=FALSE----------------------------------------------------------------
# # Preparing the data
# MyData <- coexpression.data
#
# # Reconstructing the graph
# My_graph <- graph_from_data_frame(MyData)
#
# # Extracting the vertices
# GraphVertices <- V(My_graph)
#
# # Calculating H-index
# h.index <- h_index(graph = My_graph,
# vertices = GraphVertices,
# mode = "all")
#
# head(h.index)
# #> ADAMTS9-AS2 C8orf34-AS1 CADM3-AS1 FAM83A-AS1 FENDRR LANCL1-AS1
# #> 11 9 11 2 12 12
## ----LH_index, eval=FALSE---------------------------------------------------------------
# # Preparing the data
# MyData <- coexpression.data
#
# # Reconstructing the graph
# My_graph <- graph_from_data_frame(MyData)
#
# # Extracting the vertices
# GraphVertices <- V(My_graph)
#
# # Calculating Local H-index
# lh.index <- lh_index(graph = My_graph,
# vertices = GraphVertices,
# mode = "all")
#
# head(lh.index)
# #> ADAMTS9-AS2 C8orf34-AS1 CADM3-AS1 FAM83A-AS1 FENDRR LANCL1-AS1
# #> 1165 446 994 34 1289 1265
## ----CI, eval=FALSE---------------------------------------------------------------------
# # Preparing the data
# MyData <- coexpression.data
#
# # Reconstructing the graph
# My_graph <- graph_from_data_frame(MyData)
#
# # Extracting the vertices
# GraphVertices <- V(My_graph)
#
# # Calculating Collective Influence
# ci <- collective.influence(graph = My_graph,
# vertices = GraphVertices,
# mode = "all", d=3)
#
# head(ci)
# #> ADAMTS9-AS2 C8orf34-AS1 CADM3-AS1 FAM83A-AS1 FENDRR LANCL1-AS1
# #> 9918 70560 39078 675 10716 7350
## ----CR, eval=FALSE---------------------------------------------------------------------
# # Preparing the data
# MyData <- coexpression.data
#
# # Reconstructing the graph
# My_graph <- graph_from_data_frame(MyData)
#
# # Extracting the vertices
# GraphVertices <- V(My_graph)
#
# # Calculating ClusterRank
# cr <- clusterRank(graph = My_graph,
# vids = GraphVertices,
# directed = FALSE, loops = TRUE)
#
# head(cr)
# #> ADAMTS9-AS2 C8orf34-AS1 CADM3-AS1 FAM83A-AS1 FENDRR LANCL1-AS1
# #> 63.459812 5.185675 21.111776 1.280000 135.098278 81.255195
## ----cond.prob--------------------------------------------------------------------------
# Preparing the data
MyData <- centrality.measures
# Assessing the conditional probability
My.conditional.prob <- cond.prob.analysis(data = MyData,
nodes.colname = rownames(MyData),
Desired.colname = "BC",
Condition.colname = "NC")
print(My.conditional.prob)
## ----double.cent.assess, eval=FALSE-----------------------------------------------------
# # Preparing the data
# MyData <- centrality.measures
#
# # Association assessment
# My.metrics.assessment <- double.cent.assess(data = MyData,
# nodes.colname = rownames(MyData),
# dependent.colname = "BC",
# independent.colname = "NC")
#
# print(My.metrics.assessment)
# #> $Summary_statistics
# #> BC NC
# #> Min. 0.000000000 1.2000
# #> 1st Qu. 0.000000000 66.0000
# #> Median 0.000000000 156.0000
# #> Mean 0.005813357 132.3443
# #> 3rd Qu. 0.000340000 179.3214
# #> Max. 0.529464720 192.0000
# #>
# #> $Normality_results
# #> p.value
# #> BC 1.415450e-50
# #> NC 9.411737e-30
# #>
# #> $Dependent_Normality
# #> [1] "Non-normally distributed"
# #>
# #> $Independent_Normality
# #> [1] "Non-normally distributed"
# #>
# #> $GAM_nonlinear.nonmonotonic.results
# #> edf p-value
# #> 8.992406 0.000000
# #>
# #> $Association_type
# #> [1] "nonlinear-nonmonotonic"
# #>
# #> $HoeffdingD_Statistic
# #> D_statistic P_value
# #> Results 0.01770279 1e-08
# #>
# #> $Dependence_Significance
# #> Hoeffding
# #> Results Significantly dependent
# #>
# #> $NNS_dep_results
# #> Correlation Dependence
# #> Results -0.7948106 0.8647164
# #>
# #> $ConditionalProbability
# #> [1] 55.35386
# #>
# #> $ConditionalProbability_split.half.sample
# #> [1] 55.90331
## ----double.cent.assess.noRegr., eval=FALSE---------------------------------------------
# # Preparing the data
# MyData <- centrality.measures
#
# # Association assessment
# My.metrics.assessment <- double.cent.assess.noRegression(data = MyData,
# nodes.colname = rownames(MyData),
# centrality1.colname = "BC",
# centrality2.colname = "NC")
#
# print(My.metrics.assessment)
# #> $Summary_statistics
# #> BC NC
# #> Min. 0.000000000 1.2000
# #> 1st Qu. 0.000000000 66.0000
# #> Median 0.000000000 156.0000
# #> Mean 0.005813357 132.3443
# #> 3rd Qu. 0.000340000 179.3214
# #> Max. 0.529464720 192.0000
# #>
# #> $Normality_results
# #> p.value
# #> BC 1.415450e-50
# #> NC 9.411737e-30
# #>
# #> $Centrality1_Normality
# #> [1] "Non-normally distributed"
# #>
# #> $Centrality2_Normality
# #> [1] "Non-normally distributed"
# #>
# #> $HoeffdingD_Statistic
# #> D_statistic P_value
# #> Results 0.01770279 1e-08
# #>
# #> $Dependence_Significance
# #> Hoeffding
# #> Results Significantly dependent
# #>
# #> $NNS_dep_results
# #> Correlation Dependence
# #> Results -0.7948106 0.8647164
# #>
# #> $ConditionalProbability
# #> [1] 55.35386
# #>
# #> $ConditionalProbability_split.half.sample
# #> [1] 55.68163
## ----IVI.from.indices, eval=FALSE-------------------------------------------------------
# # Preparing the data
# MyData <- centrality.measures
#
# # Calculation of IVI
# My.vertices.IVI <- ivi.from.indices(DC = MyData$DC,
# CR = MyData$CR,
# NC = MyData$NC,
# LH_index = MyData$LH_index,
# BC = MyData$BC,
# CI = MyData$CI)
#
# head(My.vertices.IVI)
# #> [1] 24.670056 8.344337 18.621049 1.017768 29.437028 33.512598
## ----IVI, eval=FALSE--------------------------------------------------------------------
# # Preparing the data
# MyData <- coexpression.data
#
# # Reconstructing the graph
# My_graph <- graph_from_data_frame(MyData)
#
# # Extracting the vertices
# GraphVertices <- V(My_graph)
#
# # Calculation of IVI
# My.vertices.IVI <- ivi(graph = My_graph, vertices = GraphVertices,
# weights = NULL, directed = FALSE, mode = "all",
# loops = TRUE, d = 3, scale = "range")
#
# head(My.vertices.IVI)
# #> ADAMTS9-AS2 C8orf34-AS1 CADM3-AS1 FAM83A-AS1 FENDRR LANCL1-AS1
# #> 39.53878 19.94999 38.20524 1.12371 100.00000 47.49356
## ----net.for.vis, eval=FALSE------------------------------------------------------------
# # Reconstructing the graph
# set.seed(70)
# My_graph <- igraph::sample_gnm(n = 50, m = 120, directed = TRUE)
#
# # Calculating the IVI values
# My_graph_IVI <- ivi(My_graph, directed = TRUE)
#
# # Visualizing the graph based on IVI values
# My_graph_IVI_Vis <- cent_network.vis(graph = My_graph,
# cent.metric = My_graph_IVI,
# directed = TRUE,
# plot.title = "IVI-based Network",
# legend.title = "IVI value")
#
# My_graph_IVI_Vis
## ----Spreading.score, eval=FALSE--------------------------------------------------------
# # Preparing the data
# MyData <- coexpression.data
#
# # Reconstructing the graph
# My_graph <- graph_from_data_frame(MyData)
#
# # Extracting the vertices
# GraphVertices <- V(My_graph)
#
# # Calculation of Spreading score
# Spreading.score <- spreading.score(graph = My_graph,
# vertices = GraphVertices,
# weights = NULL, directed = FALSE, mode = "all",
# loops = TRUE, d = 3, scale = "range")
#
# head(Spreading.score)
# #> ADAMTS9-AS2 C8orf34-AS1 CADM3-AS1 FAM83A-AS1 FENDRR LANCL1-AS1
# #> 42.932497 38.094111 45.114648 1.587262 100.000000 49.193292
## ----Hubness.score, eval=FALSE----------------------------------------------------------
# # Preparing the data
# MyData <- coexpression.data
#
# # Reconstructing the graph
# My_graph <- graph_from_data_frame(MyData)
#
# # Extracting the vertices
# GraphVertices <- V(My_graph)
#
# # Calculation of Hubness score
# Hubness.score <- hubness.score(graph = My_graph,
# vertices = GraphVertices,
# directed = FALSE, mode = "all",
# loops = TRUE, scale = "range")
#
# head(Hubness.score)
# #> ADAMTS9-AS2 C8orf34-AS1 CADM3-AS1 FAM83A-AS1 FENDRR LANCL1-AS1
# #> 84.299719 46.741660 77.441514 8.437142 92.870451 88.734131
## ----SIRIR, eval=FALSE------------------------------------------------------------------
# # Reconstructing the graph
# My_graph <- sif2igraph(Path = "Sample_SIF.sif")
#
# # Extracting the vertices
# GraphVertices <- V(My_graph)
#
# # Calculation of influence rank
# Influence.Ranks <- sirir(graph = My_graph,
# vertices = GraphVertices,
# beta = 0.5, gamma = 1, no.sim = 10, seed = 1234)
#
## ----exir.data, eval=FALSE--------------------------------------------------------------
# # Prepare sample data
# gene.names <- paste("gene", c(1:2000), sep = "_")
#
# set.seed(60)
# tp2.vs.tp1.DEGs <- data.frame(logFC = rnorm(n = 700, mean = 2, sd = 4),
# FDR = runif(n = 700, min = 0.0001, max = 0.049))
#
# set.seed(60)
# rownames(tp2.vs.tp1.DEGs) <- sample(gene.names, size = 700)
#
# set.seed(70)
# tp3.vs.tp2.DEGs <- data.frame(logFC = rnorm(n = 1300, mean = -1, sd = 5),
# FDR = runif(n = 1300, min = 0.0011, max = 0.039))
#
# set.seed(70)
# rownames(tp3.vs.tp2.DEGs) <- sample(gene.names, size = 1300)
#
# set.seed(80)
# regression.data <- data.frame(R_squared = runif(n = 800, min = 0.1, max = 0.85))
#
# set.seed(80)
# rownames(regression.data) <- sample(gene.names, size = 800)
## ----diff_data_assembl, eval=FALSE------------------------------------------------------
# my_Diff_data <- diff_data.assembly(tp2.vs.tp1.DEGs,
# tp3.vs.tp2.DEGs,
# regression.data)
#
# my_Diff_data[c(1:10),]
## ----exptl_data, eval=FALSE-------------------------------------------------------------
# set.seed(60)
# MyExptl_data <- matrix(data = runif(n = 100000, min = 2, max = 300),
# nrow = 50, ncol = 2000,
# dimnames = list(c(paste("cancer_sample", c(1:25), sep = "_"),
# paste("normal_sample", c(1:25), sep = "_")),
# gene.names))
#
# # Log transform the data to bring them closer to normal distribution
# MyExptl_data <- log2(MyExptl_data)
#
# MyExptl_data[c(1:5, 45:50),c(1:5)]
## ----condition.col, eval=FALSE----------------------------------------------------------
# MyExptl_data <- as.data.frame(MyExptl_data)
# MyExptl_data$condition <- c(rep("C", 25), rep("N", 25))
## ----ExIR, eval=FALSE-------------------------------------------------------------------
#
# #The table of differential/regression previously prepared
# my_Diff_data
#
# #The column indices of differential values in the Diff_data table
# Diff_value <- c(1,3)
#
# #The column indices of regression values in the Diff_data table
# Regr_value <- 5
#
# #The column indices of significance (P-value/FDR) values in
# # the Diff_data table
# Sig_value <- c(2,4)
#
# #The matrix/data frame of normalized experimental
# # data previously prepared
# MyExptl_data
#
# #The name of the column delineating the conditions of
# # samples in the Exptl_data matrix
# Condition_colname <- "condition"
#
# #The desired list of features
# set.seed(60)
# MyDesired_list <- sample(gene.names, size = 500) #Optional
#
# #Running the ExIR model
# My.exir <- exir(Desired_list = MyDesired_list,
# cor_thresh_method = "mr", mr = 100,
# Diff_data = my_Diff_data, Diff_value = Diff_value,
# Regr_value = Regr_value, Sig_value = Sig_value,
# Exptl_data = MyExptl_data, Condition_colname = Condition_colname,
# seed = 60, verbose = FALSE)
#
# names(My.exir)
# #> [1] "Driver table" "DE-mediator table" "Biomarker table" "Graph"
#
# class(My.exir)
# #> [1] "ExIR_Result"
## ----exir.vis, eval=FALSE---------------------------------------------------------------
# My.exir.Vis <- exir.vis(exir.results = My.exir,
# n = 5,
# y.axis.title = "Gene")
#
# My.exir.Vis
## ----comp_manipulate, eval=FALSE--------------------------------------------------------
# # Select which genes to knockout
# set.seed(60)
# ko_vertices <- sample(igraph::as_ids(V(My.exir$Graph)), size = 5)
#
# # Select which genes to up-regulate
# set.seed(1234)
# upregulate_vertices <- sample(igraph::as_ids(V(My.exir$Graph)), size = 5)
#
# Computational_manipulation <- comp_manipulate(exir_output = My.exir,
# ko_vertices = ko_vertices,
# upregulate_vertices = upregulate_vertices,
# beta = 0.5, gamma = 1, no.sim = 100, seed = 1234)
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